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Comment by vonklaus | Hacker News Reader

i think one of the one kf the problems is that articles like this; not just about ML but advanced CS and science concepts, answer a question at a complexity well above any person who would really ask it. By that I mean, if you could follow the casual explanation and complex concepts that are assumed that the reader grasps, you probably already know the difference between deep learning & ML and thus you read it out of interest to compare your view or get more information to add to your core base on the topics.

That is my 0.02, I couldn't follow this article without doing ancilliary research/wikipedia lookups at least.

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Hi @vonklaus, thanks for the feedback. This was actually not intended to be an "article" but more like an answer to a targeted question (I am the author of this little write-up). Basically, someone asked me this specific question some time ago (I think via email), and I answered it with this person's background in mind. Then, I generalized it a bit more and added it to the FAQ section in the GitHub repo in hope that it is also helpful to others. It's really more like a quick overview, idea, explanation in contrast to a fully fleshed-out blog article :)

hey thanks for the response, I am sure it is quite well done to your target audience. Do you know of any higher level material that provides a good outline conceptually but only assumes a really general knowledge? This is a bit above my comfort zone :). I do really like, i think Joel Gruus, writing. His fizzbuzz with tensor flow was hilarious. I am unfortunately not particularly gifted in mathematics so libraries like that will likely be the furthest ill go into ML/DL, oh btw whats the difference ;). jk

cheers

you mean material specific to deep learning that is more general and less math heavy? Hm, that's a good question, the resources I'd reference are all a bit math heavy. However, don't be afraid of diving into TensorFlow, it's really a nice library that takes care of all the tedious, mathematical details. E.g., in contrast/addition to NumPy (leaving out the comp. efficiency part out of the discussion for now), it already implements several optimzation algorithms, so you wouldn't have to worry about implementing backpropagation from scratch or so. Sure, it still requires a bit of linear algebra, but it's really more straight-forward than it seems at first glance :). Maybe, you'd be interested in Keras (http://keras.io); it's a wrapper around Theano and TensorFlow which provides a really intuitive interface for building neural nets! Haha, btw. I really enjoyed Joel Gruus, post ;)

great thanks. ill check out keras.io seems perf. cheers

For a good conceptual outline you should definitely check out neuralnetworksanddeeplearning.com by Michael Nielsen. Then if you want to go deeper, look for Machine Learning course on Coursera by Pedro Domingos.

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